Lemmatisation, which is one of the most important stages of text preprocessing, consists in grouping the inflected forms of a word together so they can be analysed as a single item, identified by the word’s lemma, or dictionary form. It is not a very complicated task for languages such as English, where a paradigm consists of a few forms close in spelling; but when it comes to morphologically rich languages, such as Russian, Hungarian or Irish, lemmatisation becomes more challenging. However, this task is often considered solved for most resource-rich modern languages irregardless of their morphological type. The situation is dramatically different for ancient languages characterised not only by a rich inflectional system, but also by a high level of orthographic variation, and, what is more important, a very little amount of available data. These factors make automatic morphological analysis of historical language data an underrepresented field in comparison to other NLP tasks. This work describes a case of creating an Early Irish lemmatiser with a character-level sequence-to-sequence learning method that proves efficient to overcome data scarcity. A simple character-level sequence-to-sequence model trained during 34,000 iterations reached the accuracy score of 99.2 % for known words and 64.9 % for unknown words on a rather small corpus of 83,155 samples. It outperforms both the baseline and the rule-based model described in [21] and [76] and meets the results of other systems working with historical data.
Knowledge distillation via instance-level sequence learning
